Jeffrey Ward
1c7d7d6cad
Known-classified files now flow to a second queue with its own worker pool that extracts metadata via exiftool and writes a normalized JSON sidecar next to the file in done/, leaving the original bytes untouched. - Two-stage pipeline: spool/ → classify → known/ → enrich → done/; unknowns park in unknown/ as a seam for a future pool - src/metadata.jl: exiftool -json -G -n with timeout, normalized schema (file_type, mime_type, author, created_by, dimensions, ...) + raw dump; degraded sidecar on extraction failure rather than quarantine - Sidecar-first commit so a file in done/ always has its sidecar - Parametrized worker_loop with classify/enrich handlers; blocking backpressure on a full known queue (never drop a classified file) - Stage-aware recovery: spool/ and known/ resume at their correct stage - Ordered drain: close stage-1 and wait its workers (the known queue's only producer) before closing the known queue - exiftool required at startup (fail-fast); new FS_KNOWN_*/FS_UNKNOWN_DIR/ FS_EXIFTOOL_TIMEOUT config knobs; combined-pool thread warning
253 lines
13 KiB
Markdown
253 lines
13 KiB
Markdown
# FileServer
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A minimal Julia service that receives files over HTTP and hands them off to a
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pool of worker threads for processing. The HTTP endpoint does no real work: it
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spools each uploaded file to disk, pushes a lightweight reference onto a work
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queue, and responds immediately — staying free to accept the next upload.
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The per-file "processing" runs each file through a small neural-network
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classifier that labels it **known** (a file type resembling the training set) or
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**unknown**, and logs the result. See "File classifier" below.
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## Architecture
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The pipeline is two stages, each with its own bounded queue and its own worker
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pool (tuned independently, since classification is CPU-bound and enrichment is
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process-/IO-bound):
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```
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POST /upload (multipart)
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│
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▼
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┌─────────────────┐ spool bytes to disk
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│ HTTP handler │────────────────────────► data/spool/<uuid>-<name>
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│ (Oxygen.jl) │
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└────────┬─────────┘ enqueue reference (non-blocking)
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│ │
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▼ ▼
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202 + job IDs ┌────────────────────┐
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(503 if full) │ stage-1 queue │ classification
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└─────────┬──────────┘
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│ dequeue
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┌───────────────────┼───────────────────┐
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▼ ▼ ▼
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classify wkr 1 classify wkr 2 … classify wkr N
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│
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┌────────────┴────────────┐
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:unknown :known
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│ │ move to data/known/, then
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▼ ▼ enqueue (blocking backpressure)
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data/unknown/<uuid>-<name> ┌────────────────────┐
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(parked; future pipeline) │ known queue │ enrichment
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└─────────┬──────────┘
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│ dequeue
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┌───────────────────┼───────────────────┐
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▼ ▼ ▼
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known wkr 1 known wkr 2 … known wkr M
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│ exiftool → normalized sidecar
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success ────┴──► data/done/<uuid>-<name>
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data/done/<uuid>-<name>.meta.json (sidecar-first commit)
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failure ───────► data/failed/<uuid>-<name>
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```
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Key properties:
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- **Fast intake:** the queue only ever carries small references; file bytes live
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on disk, so memory stays flat regardless of file size.
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- **Backpressure:** each queue is bounded (default 1000). When the *intake* queue
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is full, uploads get `503 Service Unavailable`. When the *known* queue is full,
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the stage-1 worker blocks and retries (a classified file is never dropped).
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- **Crash-resilient:** files survive on disk. On startup, recovery is
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stage-aware: leftovers in `data/spool/` re-enter classification and leftovers
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in `data/known/` re-enter enrichment (`recovered` / `recovered_known` in the
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log), so a file resumes at its correct stage instead of restarting from scratch.
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- **Graceful shutdown:** SIGINT (Ctrl-C) and SIGTERM (systemd/Docker/k8s `stop`)
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both stop accepting uploads, then drain the stages *in order* — close the
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stage-1 queue and wait out the classify workers (the only producer of the known
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queue) before closing the known queue and waiting out the enrich workers.
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(See "Shutdown" below for one cosmetic caveat on SIGTERM.)
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- **Safe filenames:** client-supplied names are sanitized and prefixed with a
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server-minted UUID before touching the filesystem (no path traversal).
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### Metadata enrichment (stage 2)
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Files the classifier labels **known** are handed to a second pool that extracts
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metadata with [`exiftool`](https://exiftool.org/) (`exiftool -json -G -n`) —
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chosen because no native Julia library comes close to its multi-format coverage.
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The output is normalized into a small, stable, documented schema and written as a
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JSON **sidecar** next to the file in `data/done/`, e.g.
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`data/done/<uuid>-<name>.meta.json`. The original bytes are never modified.
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> **Prerequisite:** `exiftool` must be on `PATH` (e.g. `apt install
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> libimage-exiftool-perl`). The server **fails fast at startup** if it's missing.
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Sidecar top-level fields (all nullable — present only when available), plus the
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complete raw `exiftool` object under `raw`:
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| Field | Meaning |
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|---|---|
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| `id`, `original_name` | job id and client-supplied name |
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| `file_type`, `mime_type` | e.g. `PDF` / `application/pdf` |
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| `file_size` | bytes (authoritative, from intake — not exiftool) |
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| `created_date`, `modified_date` | content timestamps |
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| `author` | person (`Author`/`Artist`/`By-line`) |
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| `created_by` | authoring app/tool (`Producer`/`CreatorTool`/`Creator`/`Software`/…) |
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| `dimensions` | `{width, height}` for media |
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| `duration` | seconds, for audio/video |
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| `page_count` | for documents |
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| `error` | set on a *degraded* sidecar (see below) |
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| `raw` | full `exiftool` output |
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Each normalized field is a coalesce over a priority list of exiftool tags
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(`src/metadata.jl`); extend a field by appending tag names. If extraction fails
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or `exiftool` times out (`FS_EXIFTOOL_TIMEOUT`, default 30s), the file still
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completes to `data/done/` with a **degraded sidecar** — `file_size`/`file_type`
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plus an `error` note — rather than being quarantined, because it's still a wanted
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known file. Only genuine I/O errors (can't write the sidecar or move the file)
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send it to `data/failed/`.
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The sidecar is committed **before** the file is moved into `data/done/`, so a
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file's presence there always implies its sidecar is already present; a crash in
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between leaves only a harmless orphan sidecar, and recovery re-enriches
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idempotently.
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## The queue seam (→ RabbitMQ later)
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The HTTP handler and workers only ever call `enqueue!`, `dequeue!`, and
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`close!` on a `JobQueue` (see `src/queue.jl`). Today that's an in-process
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`ChannelQueue`. To move to RabbitMQ (or any broker), implement a new `JobQueue`
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subtype with those three methods and swap the construction in `run` — no handler
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or worker code changes.
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## Running
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```bash
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# install deps (first time)
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julia --project=. -e 'using Pkg; Pkg.instantiate()'
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# start the server; -t sets the number of OS threads available to workers
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julia --project=. -t auto bin/server.jl
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```
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## Shutdown
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Both SIGINT and SIGTERM trigger the same idempotent graceful drain
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(stop serving → close queue → wait for workers → exit):
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- **SIGINT** is caught as an `InterruptException` (we call
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`Base.exit_on_sigint(false)`), so shutdown is clean and quiet.
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- **SIGTERM** can't be intercepted directly — Julia blocks it on worker threads
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and handles it in its own runtime, so a user `signal()` handler never fires.
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Instead we hook the drain into an `atexit` handler, which Julia's SIGTERM path
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does run. Caveat: Julia prints its own `signal 15: Terminated` backtrace
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*before* `atexit` runs. It's harmless noise — the drain still completes right
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after it — but if you want a fully quiet stop under a process manager,
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configure it to send SIGINT instead (systemd: `KillSignal=SIGINT`; Docker:
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`STOPSIGNAL SIGINT`). Give the stop timeout enough headroom to drain
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in-flight work (systemd: `TimeoutStopSec`).
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## File classifier
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Each file is scored by a fixed-structure neural network (Lux.jl) that answers a
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single binary question: is this file **known** (like the types in the training
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set) or **unknown**? It's novelty detection, not exact file-typing — it won't
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tell you "PDF", just "this looks like something I was trained on, or not".
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- **Features:** the first 16 bytes + last 16 bytes of the file, each scaled
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0–255 → `[0,1]`, giving a 32-dim input. Files under 32 bytes can't form that
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window and are classified `unknown` without touching the model.
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- **Architecture:** `Dense(32→64,relu) → Dense(64→16,relu) → Dense(16→2)`,
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raw logits; decision is `argmax` (class 1 = known, class 2 = unknown).
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- **Artifact:** trained weights live in `model/classifier.jld2` (committed), so
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the server just loads them at startup. Missing/unreadable ⇒ the server fails
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fast rather than run without classification.
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- **Effect today:** *annotate-only*. The class is logged
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(`classification=known|unknown`) but every file still moves to `done/`; the
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classifier can't misroute real files while it's unproven.
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The architecture and byte→feature mapping are defined once in `src/model.jl` and
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shared by the trainer and the server, so they can't drift apart.
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### Training
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Training is a separate, offline script — it never runs in the request path:
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```bash
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julia --project=. bin/train.jl <positives_dir> [negatives_dir]
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```
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- **positives_dir** — every file in it (≥32 bytes) is a "known" example.
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- **negatives_dir** *(optional)* — a grab-bag of *other* real file types used as
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"unknown" examples. Negatives are generated ~1:1 with positives, split 50/50
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between uniform-random byte vectors and grab-bag files. With no grab-bag dir,
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negatives are all random (weaker: the net may just learn "high entropy =
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unknown" rather than your actual types, so a grab-bag of real off-distribution
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files is recommended).
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The script uses an 80/20 seeded split, reports validation accuracy, and writes
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`model/classifier.jld2` (path overridable via `FS_MODEL_PATH`). A fixed seed
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(`FS_TRAIN_SEED`, default 42) drives negative generation, the split, and weight
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init, so the artifact is exactly regenerable from the same inputs.
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## Configuration (environment variables)
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| Variable | Default | Meaning |
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|---------------------|----------------|------------------------------------------|
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| `FS_HOST` | `127.0.0.1` | Bind address |
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| `FS_PORT` | `8080` | Port |
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| `FS_WORKERS` | `nthreads()` | Stage-1 (classification) worker tasks |
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| `FS_QUEUE_CAPACITY` | `1000` | Max pending intake jobs before `503` |
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| `FS_KNOWN_WORKERS` | `nthreads()` | Stage-2 (enrichment) worker tasks |
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| `FS_KNOWN_QUEUE_CAPACITY` | `1000` | Max pending enrichment jobs (then backpressure) |
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| `FS_SPOOL_DIR` | `data/spool` | Incoming files (pending classification) |
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| `FS_KNOWN_DIR` | `data/known` | Classified-known, awaiting enrichment |
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| `FS_UNKNOWN_DIR` | `data/unknown` | Classified-unknown, parked for a future pool |
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| `FS_DONE_DIR` | `data/done` | Enriched known files (+ `.meta.json`) |
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| `FS_FAILED_DIR` | `data/failed` | Files whose processing threw |
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| `FS_MODEL_PATH` | `model/classifier.jld2` | Classifier artifact loaded at startup |
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| `FS_EXIFTOOL_TIMEOUT` | `30` | Seconds before a stuck exiftool is killed |
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> To get real parallelism, start Julia with enough threads (`-t N`) to cover both
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> pools. If `FS_WORKERS + FS_KNOWN_WORKERS` exceeds available threads you'll get a
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> warning (non-fatal) and workers will share threads.
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## Usage
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```bash
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# health check
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curl http://127.0.0.1:8080/health
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# {"status":"ok"}
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# upload one or more files (multipart/form-data)
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curl -F "a=@report.pdf" -F "b=@data.csv" http://127.0.0.1:8080/upload
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# 202 {"accepted":[{"id":"<uuid>","name":"report.pdf"}, ...]}
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```
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Each file in a request becomes its own job. Responses:
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- `202 Accepted` — all files spooled and queued (with per-file job IDs)
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- `400 Bad Request` — not multipart, or no files present
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- `503 Service Unavailable` — queue full, retry later
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- `500 Internal Server Error` — failed to write a file to disk
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## Layout
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```
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src/
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FileServer.jl module + run() (startup, recovery, workers, serve, shutdown)
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config.jl Config struct + env parsing
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job.jl Job (the queue reference)
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queue.jl JobQueue seam + in-process ChannelQueue
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spool.jl filename sanitizing, spool/move, startup recovery
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model.jl NN architecture + byte→feature mapping (shared with trainer)
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classify.jl load artifact + classify a file at inference time
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metadata.jl exiftool extraction + normalized sidecar (stage 2)
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worker.jl parametrized worker loop + classify/enrich handlers
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server.jl HTTP routes/handlers
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bin/
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server.jl entry point
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train.jl offline training script → model/classifier.jld2
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model/
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classifier.jld2 committed trained weights (loaded at startup)
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```
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